... Starting June 1st, @Github Copilot moved to a usage-based billing model. Question for engineering leaders after this change: What's your team actually doing about it? Comment from Tyler(@tsouth2): Usage

... each one does and when you'd actually use it. 1. Find and Browse Your Brands List all brands   Pulls a full list of your brands with client name and share link for each. Useful when compiling a usage report or checking which clients have active brand links across your library. Search by name   The most common starting point. Say "use the Acme brand" and the agent finds it by name, no ID needed. It handles partial names ... ... counts for any brand and each of its variations. Useful for quarterly brand reviews, client reports, or understanding which logo version is used most. If a variation has had zero downloads in six months, that's worth knowing. Account usage

... live on Product Hunt today with the new Intrascope update. Intrascope started as a BYOK workspace for teams using multiple AI models. Now we re adding managed AI usage as well, so companies can access top AI models without managing separate API keys, vendor accounts or billing dashboards. The goal is simple: Help teams use AI in a more organized way, with permissions, spending limits, usage

... simpler. They want to use AI across the company, but they don t want to manage: API keys Provider accounts Separate billing dashboards Model setup Individual AI subscriptions Scattered chats and lost context So we re adding managed AI usage to Intrascope. With this update, teams will be able to top up their workspace and use premium AI models directly through Intrascope , without setting up their own API keys . The goal is simple: Make AI easier to adopt ... ... range of teams, not only those already familiar with APIs. We re preparing a new Product Hunt launch around this update soon, and I d love to hear your thoughts before we go live. Do you think managed AI usage

... that to a growth marketer. Or an investor. Or even your own team. Every playbook we studied was built around maximizing daily active users, increasing session length, driving habit loops. The entire vocabulary of product growth assumes that more usage = more value. For social media, that might be true. For a wellness product? It's the opposite. We had to throw out the playbook and start from scratch. Here's what we've learned so far: We stopped marketing what ... ... users who come through word-of-mouth stay longer, engage more meaningfully, and genuinely get value from the product. We're still figuring this out. Curious if anyone else is building something where success looks like less usage

... tools hide limits until you hit a wall. We tried to make Oasis usage visible up front. Insight for other users:  The  daily token usage

... agent? Per-seat? Per-query? Usage-based? Flat rate? I've changed my pricing model 4 times in 12 months. Not because I wanted to. Because the market kept telling me I was wrong and I kept not listening fast enough. This is the part of building nobody puts on their launch page. Model 1: Per-agent subscription I thought this was it. Clean. Predictable. I was genuinely proud of it, showed it to my team like "we figured ... ... system. A VP of procurement looked me in the eye on a Zoom call and said: "I genuinely don't understand what I'm paying for." I laughed it off on the call. Didn't sleep that night. Model 3: Usage

... Replit both made similar pricing changes in June. Feels like flat-rate $20/month subscriptions are structurally incompatible with agentic workloads that can burn compute 24/7. The unit economics just don't math. A few things I'm chewing on: Is usage-based or credit-based pricing now inevitable for any AI product with an agent layer? Anthropic claims Max 20x gives 20x the usage

... backwards. You only knew someone churned after they cancelled. By then, it was too late. So we looked for a leading indicator. One metric that predicted churn before it happened. The data We pulled 18 months of usage data from 1,247 accounts. We looked at every action users took in the 30 days before they cancelled. We compared them to users who stayed. The most predictive signal was not usage

... kept running into the same problem while coding with Claude and Cursor: I could feel usage climbing, but I had no clean way to see the pace in real time until after the damage was done. So I built TokenBar, a small macOS menu bar app that shows live token usage

... model works best for you, depending on the task. Different models have different strengths: some are better for quick iterations, while others excel at complex design challenges. Hover over each model option to see how they compare, including credit usage per generation. Learn how to pick the best model Build your own chatbot We noticed people like designing chatbots. You can now use the Anthropic API directly in Magic Patterns. Use it to create a working chatbot or support other ... ... works out of the box by using Magic Patterns as a Connector within Magic Patterns. Simply connect the Magic Patterns MCP as a Connector , then share any Magic Patterns link in your prompts. Learn more about Cross-Project referencing . Usage

... kept testing prompts across Claude and ChatGPT, then checking usage later and realizing I had basically no feel for where the tokens were going. That was the whole reason I built TokenBar for Mac. It sits in the menu bar and shows token usage

... Last month I got one of those AI invoices that makes you open the usage page and squint. The annoying part was not that I used tokens. It was that I had almost no visibility while I was actually building. I was jumping between apps, prompts, and models, and the cost only became obvious after the damage was done. So I built TokenBar for macOS. It sits in the menu bar and shows token usage

... thought we knew We assumed enterprise customers wanted the same things as our small business users, just more of it. More security. More control. More features. We never talked to them. We read blog posts. We looked at competitor pricing pages. We guessed. Here's what we missed: Procurement cycles. Enterprise deals take 6-12 months. We had 30-day sales cycles. We weren't built for that. Security reviews. We needed SOC2. We didn't have it. Customers asked ... ... deal died in legal review. We weren't an enterprise company. We were a small SaaS with a big ego. What the data said We went back and looked at our own analytics. Feature Build time Customer requests Actual usage

... Feature requests. Sales calls. "It would be great if you added X." We built X. Nobody used it. So we stopped trusting what people said and started tracking what they actually did. The dataset We pulled 12 months of usage data from Rankfender. 1,247 active users. 27 distinct features across 8 modules. We looked at three metrics for each feature: Adoption rate % of users who tried it at least once Retention rate % who used it again after 30 days ... ... users tried it. 3% came back. We spent 4 months building something people asked for but didn't actually stick with. The signals we track now Before building anything, we now look at three things: 1. Usage

... everyone! I just shipped TokenBar a native macOS menu bar app that tracks your AI token usage in real-time across all major providers (OpenAI, Anthropic, Google, etc.). If you're a developer spending money on AI APIs, this gives you instant visibility into what you're burning through right from your menu bar. No more surprise bills. Key features: - Real-time token counting across all providers - Lives in your macOS menu bar zero friction - Tracks costs automatically - $5 lifetime ...

... hard. People hate change. Replacing an intuitive pricing model with a less precise one makes people mad . But that's what Windsurf has done : What's changing: No more credits. Your Free, Pro, or Teams plan includes a usage allowance that refreshes automatically on a daily and weekly basis. For the majority of users, this quota will be enough to fully cover all agent usage

... should get users to their first meaningful result. If they don't experience value quickly, they will churn before paying. Whatever gets them to that "aha, this works" moment should be free. 2. Usage-Based Gates Work Better Than Feature Gates - Instead of hiding features, limit usage

... subscriptions: Claude Pro, Cursor Pro, ChatGPT Plus, Copilot, and Gemini Advanced. $90/month total. I assumed I was using all of them heavily. Turns out I was wrong. When I built TokenBar (a menu bar app that tracks AI usage limits), the first thing I noticed was that my Gemini Advanced usage

... multiple AI coding tools, you've probably hit this: You're deep in a session with Claude Code. You're on a roll. Then: "You've reached your usage limit." No warning. No countdown. Just a wall. Now what? Switch to Cursor? Check if ChatGPT still has capacity? How close are you to Copilot's limit? You don't know. None of these tools show you a unified view of where you stand. I was spending $90/month across ... ... tools and had zero visibility into my actual usage

... that came from manual research . Going through company websites, product docs, and recent launches. Looking closely at how they price their product. After doing this across ~50 companies, patterns start showing up. API products launching. Teams moving to usage-based pricing. Pricing tables getting messy. Now tools start becoming useful. Apollo helps you reach the right people. Clay helps you scale the signals. But the insight still comes from manual research. Right now the best setup seems ...

... install on a more vulnerable family member/friend's computer to help prevent them from falling victim to scams. Comment from Ryan Hendrickson(@ryan_hendrickson): @maria_vannicola  Nice! Was there something that brought you to use Firebase over other alternatives? I agree with your decision to focus on building the network first, which will make it easier to open doors to monetization later. Comment from Ryan Hendrickson(@ryan_hendrickson): @robert_vassov  Love that you took the path of keeping ... ... still picking up steam? Comment from Ryan Hendrickson(@ryan_hendrickson): @sebastianwall  Very interesting! So the primary (only?) interface with your app is via an AI Agent? Comment from Ryan Hendrickson(@ryan_hendrickson): @zigapotoc  Interesting insight about usage

... Building an AI SaaS and hitting a wall I didn't expect. We charge customers based on usage (credits). But tracking actual LLM costs per customer is a nightmare: - OpenAI bills us in aggregate, not per-customer - Customer A uses GPT-4, Customer B uses 3.5 same "credit," wildly different cost - No way to set hard spending limits that actually block ... ... requests - End of the month = spreadsheet hell, trying to reconcile Talked to a few other founders, and everyone seems to be either: 1. Eating the cost variance and hoping margins hold 2. Building janky internal tracking 3. Just... not doing usage

... changes, not just pricing tweaks. It s something I m starting to seriously think about for my own product. In particular, AI usage has real COGs (every prompt costs money), and I m seeing more platforms experimenting with usage

... Problem: Most SaaS affiliate programs pay low commissions (5 15%) or require high-volume traffic to generate meaningful income. Remote work tools often lack referral incentives aligned with actual product usage. Solution: Remote.Team offers a fixed $200 payout per qualified referral. A referral qualifies when: A team signs up via your link Posts 1,000 comments in 30 days Maintains <5% duplicate/spam messages Passes manual review for engagement quality Key details: Program activates automatically after 30 days of your ... ... platform usage